Important

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Confluent Cloud Quick Start

This quick start shows you how to get up and running with Confluent Cloud. This quick start will show the basics of using Confluent Cloud, including creating topics and producing and consuming to a Apache Kafka® cluster in Confluent Cloud.

Confluent Cloud is a resilient, scalable streaming data service based on Apache Kafka®, delivered as a fully managed service. Confluent Cloud has a web interface and local command line interface. You can manage cluster resources, settings, and billing with the web interface. You can use Confluent Cloud CLI to create and manage Kafka topics.

For more information about Confluent Cloud, see the Confluent Cloud documentation.

Prerequisites

• Access to Confluent Cloud
• Confluent Cloud Limits and Supported Features
• Maven to compile the client Java code

Step 1: Create Kafka Cluster in Confluent Cloud

Important

This step is for Confluent Cloud users only. Confluent Cloud Enterprise users can skip to Step 2: Install and Configure the Confluent Cloud CLI.

1. Log into Confluent Cloud at https://confluent.cloud.

2. Click Create cluster.

   

3. Specify a cluster name, choose a cloud provider, and click Continue. Optionally, you can specify read and write throughput, storage, region, and durability.

   

4. Confirm your cluster subscription details, payment information, and click Save and launch cluster.

   

Step 2: Install and Configure the Confluent Cloud CLI

After you have a working Kafka cluster in Confluent Cloud, you can use the Confluent Cloud command line tool to interact with your cluster from your laptop. This quick start assumes your are configuring Confluent Cloud for Java clients. You can also use Confluent Cloud with librdkafka-based clients. For more information about installing the Confluent Cloud CLI, see Install the Confluent Cloud CLI.

1. From the Environment overview page, click your cluster name.

   

2. Click Data In/Out in the sidebar and click CLI. Follow the on-screen Confluent Cloud installation instructions.

   

Step 3: Configure Confluent Cloud Schema Registry

Important

• Confluent Cloud Schema Registry is currently available as a preview. For more information, see Confluent Cloud Schema Registry Preview.
• Your VPC must be able to communicate with the Confluent Cloud Schema Registry public internet endpoint. For more information, see Using Confluent Cloud Schema Registry in a VPC Peered Environment.

Enable Schema Registry for your environment

1. From the Environment Overview page, click SCHEMA REGISTRY.

2. Click Enable Schema Registry.

   

3. Choose your geographic region and click Continue.

   

Configure the Confluent Cloud CLI for Schema Registry

1. From the Environment Overview page, click CLUSTERS and select your cluster.

   Tip

   You can view Confluent Cloud Schema Registry usage and API access information from the Environment Overview -> SCHEMA REGISTRY page.

   

2. Select Data In/Out -> Clients and the JAVA tab. Follow the onscreen instructions to create the Schema Registry-specific Java configuration, including API key pairs for Schema Registry and your Kafka cluster.

   

3. Copy this information and paste in your Confluent Cloud CLI configuration file (~/.ccloud/config).

   Your configuration file should resemble this.

   cat ~/.ccloud/config
   ssl.endpoint.identification.algorithm=https
   sasl.mechanism=PLAIN
   request.timeout.ms=20000
   bootstrap.servers=<bootstrap-server-url>
   retry.backoff.ms=500
   sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username="<kafka-api-key>" password="<kafka-api-secret>";
   security.protocol=SASL_SSL
   
   // Schema Registry specific settings
   basic.auth.credentials.source=USER_INFO
   schema.registry.basic.auth.user.info=<schema-registry-api-key>:<schema-registry-api-secret>
   schema.registry.url=<schema-registry-url>
   
   // Enable Avro serializer with Schema Registry (optional)
   key.serializer=io.confluent.kafka.serializers.KafkaAvroSerializer
   value.serializer=io.confluent.kafka.serializers.KafkaAvroSerializer
   

   Tip

   If Schema Registry credentials are not properly configured, you will get error messages when you run a producer from the Java client examples. The messages will indicate a problem registering the schema even if the schema is valid. Make sure Schema Registry credentials and URL are correct and properly formatted to match the ~/.ccloud/config examples.

4. Optional: Verify that your Schema Registry credentials are properly configured, where Schema Registry API key (<schema-registry-api-key>), API secret (<schema-registry-api-secret>), and endpoint (<schema-registry-url>) are specified.

   Run this command to authenticate with the cluster and list the topics registered in your schema.

   curl -u <schema-registry-api-key>:<schema-registry-api-secret> \
   <schema-registry-url>/subjects
   

   If no subjects are created, your output will be empty ([]). If you have subjects, your output should resemble:

   ["test2-value"]
   

   Here is an example command:

   curl -u schemaregistry5000:alsdkjaslkdjqwemnoilbkjerlkqj123123opwrqpru \
   https://psrc-lq2dm.us-east-2.aws.confluent.cloud/subjects
   

Step 4: Create Topics and Produce and Consume to Kafka

1. Create a topic named my_topic with default options.

   ccloud topic create my_topic
   

   Tip

   By default the Confluent Cloud CLI creates topics with a replication factor of 3.

2. Optional: Describe the my_topic topic.

   ccloud topic describe my_topic
   

   Your output should resemble:

   Topic:my_topic  PartitionCount:12       ReplicationFactor:3     Configs:message.format.version=1.0-IV0,max.message.bytes=2097164,min.insync.replicas=2
       Topic: my_topic     Partition: 0    Leader: 3       Replicas: 3,1,2 Isr: 3,1,2
       Topic: my_topic     Partition: 1    Leader: 0       Replicas: 0,2,3 Isr: 0,2,3
       Topic: my_topic     Partition: 2    Leader: 1       Replicas: 1,3,0 Isr: 1,3,0
       Topic: my_topic     Partition: 3    Leader: 2       Replicas: 2,0,1 Isr: 2,0,1
       Topic: my_topic     Partition: 4    Leader: 3       Replicas: 3,2,0 Isr: 3,2,0
       Topic: my_topic     Partition: 5    Leader: 0       Replicas: 0,3,1 Isr: 0,3,1
       Topic: my_topic     Partition: 6    Leader: 1       Replicas: 1,0,2 Isr: 1,0,2
       Topic: my_topic     Partition: 7    Leader: 2       Replicas: 2,1,3 Isr: 2,1,3
       Topic: my_topic     Partition: 8    Leader: 3       Replicas: 3,0,1 Isr: 3,0,1
       Topic: my_topic     Partition: 9    Leader: 0       Replicas: 0,1,2 Isr: 0,1,2
       Topic: my_topic     Partition: 10   Leader: 1       Replicas: 1,2,3 Isr: 1,2,3
       Topic: my_topic     Partition: 11   Leader: 2       Replicas: 2,3,0 Isr: 2,3,0
   

3. Modify the my_topic topic to have a retention period of days (259200000 milliseconds).

   ccloud topic alter my_topic --config="retention.ms=259200000"
   

   Your output should resemble:

   Topic configuration for "my_topic" altered.
   

4. Produce records to the my_topic topic.

   ccloud produce --topic my_topic
   

   You can type messages in as standard input. By default they are newline separated. Press Ctrl + C to exit.

   foo
   bar
   baz
   ^C
   

5. Consume items from the my_topic topic and press Ctrl + C to exit.

   ccloud consume -b -t my_topic
   

   Your output should show the items that you entered in ccloud produce:

   baz
   foo
   bar
   ^C
   Processed a total of 3 messages.
   

   The order of the consumed messages does not match the order that they were produced. This is because the producer spread them over the 10 partitions in the my_topic topic and the consumer reads from all 10 partitions in parallel.

Step 5: Run Java Examples

In this step you clone the Examples repository from GitHub and run Confluent Cloud Java examples with Avro. The examples repository contains demo applications and code examples for Confluent Platform and Kafka.

1. Clone the Confluent Cloud examples repository from GitHub and navigate to the Confluent Cloud Java directory.

   git clone https://github.com/confluentinc/examples.git
   cd examples/clients/cloud/java
   

2. Build the client example.

   mvn clean package
   

3. Run the producer (with arguments that specify the path to connect to your local Confluent Cloud instance and topic name).

   mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ProducerAvroExample" \
   -Dexec.args="$HOME/.ccloud/config my_topic_avro"
   

   Tip

   If Schema Registry credentials are not properly configured, you will get error messages when you run a producer from the Java client examples. The messages will indicate a problem registering the schema even if the schema is valid. Make sure Schema Registry credentials and URL are correct and properly formatted to match the ~/.ccloud/config examples.

4. Run the Kafka consumer application to read the records that were just published to the Kafka cluster, and display the records in the console.

   • Rebuild the example.

     mvn clean package
     

   • Run the consumer (with arguments that specify the path to the Confluent Cloud instance and topic name).

     mvn exec:java -Dexec.mainClass="io.confluent.examples.clients.cloud.ConsumerAvroExample" \
     -Dexec.args="$HOME/.ccloud/config my_topic_avro"
     

   • Hit Ctrl+C to stop.

5. View the schema information registered in Confluent Cloud Schema Registry, where Schema Registry API key (<schema-registry-api-key>), API secret (<schema-registry-api-secret>), and endpoint (<schema-registry-url>) are specified.

   • View the list of registered subjects.

     curl -u <schema-registry-api-key>:<schema-registry-api-secret> \
     <schema-registry-url>/subjects/my_topic_avro-value/versions/1
     

     Your output should resemble:

     {"subject":"my_topic_avro","version":1,"id":100001,"schema":"{\"name\":\"io.confluent.examples.clients.cloud.DataRecordAvro\",\"type\":\"record\",\"fields\":[{\"name\":\"count\",\"type\":\"long\"}]}"}
     

6. View the list of topics.

   ccloud topic list
   

   Your output should show:

   my_topic
   my_topic_avro
   

7. Delete the topics my_topic and my_topic_avro.

   Caution

   Use this command carefully as data loss can occur.

   ccloud topic delete my_topic
   ccloud topic delete my_topic_avro
   

   Your output should resemble:

   Topic "my_topic" marked for deletion.
   Topic "my_topic_avro" marked for deletion.
   

Next Steps

• Connect your components and data to Confluent Cloud
• Configure Multi-Node Environment
• Learn more about Confluent Cloud in the documentation